
import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor







class 				UpperCAmelCase						:

					def __init__(      self   :       Optional[int]			,   lowerCAmelCase   :       List[Any]			,   lowerCAmelCase   :       Any=13			,   lowerCAmelCase   :       Optional[Any]=30			,   lowerCAmelCase   :       Tuple=2			,   lowerCAmelCase   :       Optional[Any]=3			,   lowerCAmelCase   :       int=True			,   lowerCAmelCase   :       Optional[Any]=True			,   lowerCAmelCase   :       Tuple=32			,   lowerCAmelCase   :       List[str]=5			,   lowerCAmelCase   :       str=4			,   lowerCAmelCase   :       Optional[Any]=37			,   lowerCAmelCase   :       List[Any]="gelu"			,   lowerCAmelCase   :       List[str]=0.1			,   lowerCAmelCase   :       Tuple=0.1			,   lowerCAmelCase   :       List[Any]=10			,   lowerCAmelCase   :       List[str]=0.02			,   lowerCAmelCase   :       str=None			,   lowerCAmelCase   :       Dict=2			,   ):
											lowercase							:     Optional[int]									=	parent
											lowercase							:     Optional[Any]									=	batch_size
											lowercase							:     Optional[Any]									=	image_size
											lowercase							:     Optional[int]									=	patch_size
											lowercase							:     Optional[Any]									=	num_channels
											lowercase							:     str									=	is_training
											lowercase							:     Union[str, Any]									=	use_labels
											lowercase							:     List[str]									=	hidden_size
											lowercase							:     Dict									=	num_hidden_layers
											lowercase							:     List[Any]									=	num_attention_heads
											lowercase							:     Optional[int]									=	intermediate_size
											lowercase							:     List[Any]									=	hidden_act
											lowercase							:     str									=	hidden_dropout_prob
											lowercase							:     Tuple									=	attention_probs_dropout_prob
											lowercase							:     Any									=	type_sequence_label_size
											lowercase							:     str									=	initializer_range
											lowercase							:     Tuple									=	scope
											lowercase							:     Optional[int]									=	encoder_stride

											# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											lowercase							:     Tuple									=	(image_size // patch_size) ** 2
											lowercase							:     Optional[int]									=	num_patches + 1

					def 							_lowerCAmelCase						(      self   :       List[Any]       ):
											lowercase							:     Tuple									=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											lowercase							:     List[Any]									=	None
											if self.use_labels:
																	lowercase							:     List[Any]									=	ids_tensor([self.batch_size]			,   self.type_sequence_label_size       )

											lowercase							:     str									=	self.get_config()

											return config, pixel_values, labels

					def 							_lowerCAmelCase						(      self   :       str       ):
											return ViTConfig(
											    image_size=self.image_size			,   patch_size=self.patch_size			,   num_channels=self.num_channels			,   hidden_size=self.hidden_size			,   num_hidden_layers=self.num_hidden_layers			,   num_attention_heads=self.num_attention_heads			,   intermediate_size=self.intermediate_size			,   hidden_act=self.hidden_act			,   hidden_dropout_prob=self.hidden_dropout_prob			,   attention_probs_dropout_prob=self.attention_probs_dropout_prob			,   is_decoder=SCREAMING_SNAKE_CASE_			,   initializer_range=self.initializer_range			,   encoder_stride=self.encoder_stride			,   )

					def 							_lowerCAmelCase						(      self   :       Optional[Any]			,   lowerCAmelCase   :       Optional[Any]			,   lowerCAmelCase   :       Tuple			,   lowerCAmelCase   :       List[str]       ):
											lowercase							:     Optional[Any]									=	ViTModel(config=SCREAMING_SNAKE_CASE_       )
											model.to(SCREAMING_SNAKE_CASE_       )
											model.eval()
											lowercase							:     Optional[Any]									=	model(SCREAMING_SNAKE_CASE_       )
											self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)       )

					def 							_lowerCAmelCase						(      self   :       List[str]			,   lowerCAmelCase   :       Any			,   lowerCAmelCase   :       List[Any]			,   lowerCAmelCase   :       Optional[int]       ):
											lowercase							:     int									=	ViTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_       )
											model.to(SCREAMING_SNAKE_CASE_       )
											model.eval()
											lowercase							:     str									=	model(SCREAMING_SNAKE_CASE_       )
											self.parent.assertEqual(
											    result.reconstruction.shape			,   (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

											# test greyscale images
											lowercase							:     Union[str, Any]									=	1
											lowercase							:     str									=	ViTForMaskedImageModeling(SCREAMING_SNAKE_CASE_       )
											model.to(SCREAMING_SNAKE_CASE_       )
											model.eval()

											lowercase							:     Optional[int]									=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											lowercase							:     str									=	model(SCREAMING_SNAKE_CASE_       )
											self.parent.assertEqual(result.reconstruction.shape			,   (self.batch_size, 1, self.image_size, self.image_size)       )

					def 							_lowerCAmelCase						(      self   :       Tuple			,   lowerCAmelCase   :       Dict			,   lowerCAmelCase   :       Union[str, Any]			,   lowerCAmelCase   :       Any       ):
											lowercase							:     str									=	self.type_sequence_label_size
											lowercase							:     List[Any]									=	ViTForImageClassification(SCREAMING_SNAKE_CASE_       )
											model.to(SCREAMING_SNAKE_CASE_       )
											model.eval()
											lowercase							:     Dict									=	model(SCREAMING_SNAKE_CASE_			,   labels=SCREAMING_SNAKE_CASE_       )
											self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											lowercase							:     Union[str, Any]									=	1
											lowercase							:     str									=	ViTForImageClassification(SCREAMING_SNAKE_CASE_       )
											model.to(SCREAMING_SNAKE_CASE_       )
											model.eval()

											lowercase							:     Dict									=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											lowercase							:     str									=	model(SCREAMING_SNAKE_CASE_       )
											self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)       )




					def 							_lowerCAmelCase						(      self   :       str       ):
											lowercase							:     Any									=	self.prepare_config_and_inputs()
											(
											    lowercase							
											)							:     List[Any]									=	config_and_inputs
											lowercase							:     Union[str, Any]									=	{"""pixel_values""": pixel_values}
											return config, inputs_dict







@require_torch
class 				UpperCAmelCase						( _lowerCAmelCase    ,			_lowerCAmelCase    ,			unittest.TestCase       ):
					a__:			Any      	=					(
					    (
					        ViTModel,
					        ViTForImageClassification,
					        ViTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					a__:			Optional[Any]      	=					(
					    {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
					    if is_torch_available()
					    else {}
					)
					a__:			List[str]      	=					True

					a__:			Tuple      	=					False
					a__:			List[Any]      	=					False
					a__:			List[Any]      	=					False

					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											lowercase							:     Any									=	ViTModelTester(self       )
											lowercase							:     List[str]									=	ConfigTester(self			,   config_class=SCREAMING_SNAKE_CASE_			,   has_text_modality=SCREAMING_SNAKE_CASE_			,   hidden_size=37       )

					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											self.config_tester.run_common_tests()

					@unittest.skip(reason='''ViT does not use inputs_embeds'''       )
					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											pass

					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											lowercase							:     Optional[int]									=	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase							:     str									=	model_class(SCREAMING_SNAKE_CASE_       )
																	self.assertIsInstance(model.get_input_embeddings()			,   (nn.Module)       )
																	lowercase							:     int									=	model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_			,   nn.Linear       )       )

					def 							_lowerCAmelCase						(      self   :       str       ):
											lowercase							:     List[str]									=	self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase							:     Union[str, Any]									=	model_class(SCREAMING_SNAKE_CASE_       )
																	lowercase							:     Dict									=	inspect.signature(model.forward       )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase							:     Tuple									=	[*signature.parameters.keys()]

																	lowercase							:     Tuple									=	["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]			,   SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Optional[Any]       ):
											lowercase							:     str									=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											lowercase							:     Tuple									=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_       )

					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											lowercase							:     Optional[Any]									=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )




					@slow
					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase							:     Union[str, Any]									=	ViTModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )





def 							lowerCamelCase_   (      ):
						lowercase							:     str									=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
						return image







@require_torch
@require_vision
class 				UpperCAmelCase						( unittest.TestCase       ):

					@cached_property
					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''       ) if is_vision_available() else None

					@slow
					def 							_lowerCAmelCase						(      self   :       List[str]       ):
											lowercase							:     Union[str, Any]									=	ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''       ).to(SCREAMING_SNAKE_CASE_       )

											lowercase							:     int									=	self.default_image_processor
											lowercase							:     Optional[int]									=	prepare_img()
											lowercase							:     List[Any]									=	image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       ).to(SCREAMING_SNAKE_CASE_       )

											# forward pass
											with torch.no_grad():
																	lowercase							:     Optional[int]									=	model(**SCREAMING_SNAKE_CASE_       )

											# verify the logits
											lowercase							:     Optional[Any]									=	torch.Size((1, 1000)       )
											self.assertEqual(outputs.logits.shape			,   SCREAMING_SNAKE_CASE_       )

											lowercase							:     Tuple									=	torch.tensor([-0.27_44, 0.82_15, -0.08_36]       ).to(SCREAMING_SNAKE_CASE_       )

											self.assertTrue(torch.allclose(outputs.logits[0, :3]			,   SCREAMING_SNAKE_CASE_			,   atol=1E-4       )       )

					@slow
					def 							_lowerCAmelCase						(      self   :       Any       ):
											# ViT models have an `interpolate_pos_encoding` argument in their forward method,
											# allowing to interpolate the pre-trained position embeddings in order to use
											# the model on higher resolutions. The DINO model by Facebook AI leverages this
											# to visualize self-attention on higher resolution images.
											lowercase							:     Union[str, Any]									=	ViTModel.from_pretrained('''facebook/dino-vits8'''       ).to(SCREAMING_SNAKE_CASE_       )

											lowercase							:     int									=	ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''			,   size=480       )
											lowercase							:     int									=	prepare_img()
											lowercase							:     List[Any]									=	image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       )
											lowercase							:     List[Any]									=	inputs.pixel_values.to(SCREAMING_SNAKE_CASE_       )

											# forward pass
											with torch.no_grad():
																	lowercase							:     int									=	model(SCREAMING_SNAKE_CASE_			,   interpolate_pos_encoding=SCREAMING_SNAKE_CASE_       )

											# verify the logits
											lowercase							:     Any									=	torch.Size((1, 3601, 384)       )
											self.assertEqual(outputs.last_hidden_state.shape			,   SCREAMING_SNAKE_CASE_       )

											lowercase							:     Optional[int]									=	torch.tensor(
											    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]       ).to(SCREAMING_SNAKE_CASE_       )

											self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]			,   SCREAMING_SNAKE_CASE_			,   atol=1E-4       )       )




					@slow
					@require_accelerate
					@require_torch_gpu
					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											lowercase							:     str									=	ViTModel.from_pretrained('''facebook/dino-vits8'''			,   torch_dtype=torch.floataa			,   device_map='''auto'''       )
											lowercase							:     str									=	self.default_image_processor

											lowercase							:     Any									=	prepare_img()
											lowercase							:     List[str]									=	image_processor(images=SCREAMING_SNAKE_CASE_			,   return_tensors='''pt'''       )
											lowercase							:     Optional[int]									=	inputs.pixel_values.to(SCREAMING_SNAKE_CASE_       )

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																	lowercase							:     List[Any]									=	model(SCREAMING_SNAKE_CASE_       )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






